Method and System for Presenting Targeted Advertisements

ABSTRACT

A method and system for presenting targeted advertisements to a subscriber includes extracting probabilistic information about subscriber activities from one or more source and processing the probabilistic information about subscriber activities to generate a subscriber characterization vector.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/109,734, filed May 17, 2011, and entitled Method and System forPresenting Targeted Advertisements, which is a continuation of U.S.application Ser. No. 09/591,577, filed Jun. 9, 2000, now U.S. Pat. No.7,949,565, and entitled Privacy Protected Advertising System, whichclaims the benefit of U.S. Provisional Application Nos. 60/183,409,filed Feb. 18, 2000, entitled Ad Matching Service; 60/190,341, filedMar. 16, 2000, entitled Privacy Protected Filtering and ProfilingSystem; and 60/196,375, filed Apr. 12, 2000, entitled Ad MatchingService. U.S. application Ser. No. 09/591,577 is a Continuation-in-partof U.S. application Ser. No. 09/204,888, filed Dec. 3, 1998, andentitled Subscriber Characterization System, now U.S. Pat. No.7,150,030. The entire disclosures of all of the above applications areincorporated herein by reference.

This application is related to U.S. application Ser. No. 09/205,653,filed Dec. 3, 1998, now U.S. Pat. No. 6,457,010; Ser. No. 09/205,119,filed Dec. 3, 1998; Ser. No. 09/268,519, filed Mar. 12, 1999, now U.S.Pat. No. 6,298,348; Ser. No. 09/268,526, filed Mar. 12, 1999, now U.S.Pat. No. 6,216,129; and Ser. No. 09/268,520, filed Mar. 12, 1999, nowU.S. Pat. No. 6,324,519. All of the above applications are incorporatedherein by reference in their entirety, but are not admitted to be priorart.

BACKGROUND OF THE INVENTION

Advertising forms an important part of broadcast programming includingbroadcast video (television), radio and printed media. The revenuesgenerated from advertisers subsidize and in some cases pay entirely forprogramming received by subscribers. For example, over the air broadcastprogramming (non-cable television) is provided entirely free tosubscribers and is essentially paid for by the advertisements placed inthe shows that are watched. Even in cable television systems andsatellite-based systems, the revenues from advertisements subsidize thecost of the programming, and were it not for advertisements, the monthlysubscription rates for cable television would be many times higher thanat present. Radio similarly offers free programming based on paymentsfor advertising. The low cost of newspapers and magazines is based onthe subsidization of the cost of reporting, printing and distributionfrom the advertising revenues.

Techniques for inserting pre-recorded spot messages into broadcasttransmission have been known. Generally, broadcast video sources (i.e.,TV networks, special interest channels, etc.) schedule their air timewith two types of information: “programming” for the purpose ofinforming or entertaining, and “avails” for the purpose of advertising.The avails may occupy roughly 20-25% of the total transmitting time, andare usually divided into smaller intervals of 15, 30, or 60 seconds.

In many prior art systems, the insertion of advertisements in avails ishandled by a combination of cue-tone detectors, switching equipment andtape players that hold the advertising material. Upon receipt of the cuetones, an insertion controller automatically turns on a tape playercontaining the advertisement. Switching equipment then switches thesystem output from the video and audio signals received from theprogramming source to the output of the tape player. The tape playerremains on for the duration of the advertising, after which theinsertion controller causes the switching equipment to switch back tothe video and audio channels of the programming source. When switched,these successive program and advertising segments usually feed to aradio-frequency (RF) modulator for delivery to the subscribers.

Many subscriber television systems, such as cable television arecurrently being converted to digital systems. These new digital systemscompress the advertising data according to decompression standards, suchas a Motion Picture Experts Group (MPEG) compression standard (currentlyMPEG-2 standard). The compressed data is then stored as a digital fileon a large disk drive (or several drives). Upon receipt of the cue tone,the digital file is spooled (“played”) off of the drive.

The advertisement may be inserted into the digital MPEG stream usingdigital video splicing techniques that include the healing of the brokenMPEG stream. Alternatively, the digital advertisement may be convertedto analog and spliced with an analog signal. Yet another technique forad insertion involves decompressing the digital MPEG stream and splicingthe ad in with the program in an uncompressed format.

A prior art (present model) of providing advertisements along withactual programming is based on linked sponsorship. In the linkedsponsorship model, the advertisements are inserted into the actualprogramming based on the contents of the programming, e.g., a babystroller advertisement may be inserted into a parenting program.

Even with linked sponsorship, advertising, and in particular broadcasttelevision advertising, is mostly ineffective. That is, a largepercentage, if not the majority of advertisements, do not have a highprobability of effecting a sale. In addition to this fact, manyadvertisements are not even seen/heard by the subscriber who may mutethe sound, change channels, or simply leave the room during a commercialbreak.

The reasons for such ineffectiveness are due to the fact that thedisplayed advertisements are not targeted to the subscribers' needs,likes or preferences. Generally, the same advertisements are displayedto all the subscribers irrespective of the needs and preferences of thesubscribers.

In the Internet world, efforts have been made to collect informationabout subscriber likes and preferences by different means, e.g., by theuse of cookies. In cookies and other profiling means, the user viewinghabits, purchase habits, or surfing habits are monitored, recorded andanalyzed, and then, based on the analysis, suitable advertisements areselected. Even though cookies and other profiling means assist intargeting advertising, they have recently come under fire as these meansare known to invade the privacy of the subscribers without theirauthorization.

Thus, a system and a method is desired for providingsubscribers/consumers with advertisements which are moretargeted/directed to their lifestyles, while ensuring that theirdemographic, purchase, and product preference data is maintainedprivate.

SUMMARY OF THE INVENTION

The present invention is directed at a system and a method for providingsubscribers/consumers with advertisements that are more directed totheir lifestyles, while ensuring that their demographic, purchase, andproduct preference data is maintained private. The present inventionallows manufacturers and advertisers to use their advertising dollarsmore effectively across a multitude of media platforms including videoand Internet domains, and eventually extending into the printed media.

The system is based on the premise that the subscribers may agree tohave advertisements delivered to them on a more selective basis than theprior art “linked sponsorship” model in which advertisements are onlylinked to the contents of the programming. Subscribers/consumers whosign up for this service will receive discounts from the Internet accessor video service provider. Advertisers may send profiles for theiradvertisements to a Secure Correlation Server™ (SCS) that allows theadvertisement to be correlated to the subscriber profiles. Noinformation regarding the subscriber is released, and subscribers who donot wish to participate in the service are not profiled.

A system in accordance with one embodiment of the present inventionutilizes the principles of Quantum Advertising™ in whichsubscribers/consumers are described by consumer/subscribercharacterization vectors that contain deterministic and probabilisticinformation regarding the consumer/subscriber, but do not containprivacy violating information such as, transaction records of purchases,video selections, or other raw data.

In accordance with the principles of one embodiment of the presentinvention, the subscriber profiles may be created by collectinginformation from a plurality of distributed databases. These distributeddatabases may be queried through the use of operators that in effectmake measurements on certain “observables.” By controlling the types ofobservables, certain parameters may be measured (in a probabilistic ordeterministic sense) while other parameters may remain unmeasurable inorder to preserve privacy. The operators may include clusteringoperators as well as operators for correlating advertisementcharacterization vectors with consumer/subscriber characterizations.

In another embodiment of the present invention, a system permits thetargeting of advertisements in the Internet and video platforms, e.g.,Switched Digital Video (SDV) and cable-based systems. In a SDV platform,the present invention allows for resolution of the advertising at thelevel of the home and even at the level of the individualuser/subscriber. The system of the present invention may also beutilized for the delivery of advertisements over cable networks byselecting advertisements at the head end or substituting advertisementsin the set-top box.

The general principles of the present invention are not constrained tovideo networks and may be generally applied to a variety of mediasystems including printed media, radio broadcasting, and store coupons.The system provides the overall capability to match advertisements usingconsumer profiles that do not contain the raw transaction information,thus subscriber privacy is maintained.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and objects of the invention will be more fullyunderstood from the following detailed description of the preferredembodiments that should be read in light of the accompanying drawings:

FIG. 1A illustrates advertisement applicability modeled as adistribution curve;

FIG. 1B illustrates an exemplary case of targeted marketing, wheresubscribers are divided into subgroups and the advertisement isdisplayed only to a subgroup of the subscribers;

FIG. 1C illustrates an exemplary case where different success rates aredetermined by measuring products or services that were purchased as theresult of the viewing of a targeted advertisement;

FIG. 2 illustrates an exemplary television system based on traditionaladvertising schemes;

FIG. 3 illustrates a system utilizing targeted advertisements based onthe principles of the present invention;

FIG. 4 illustrates a context diagram for a subscriber characterizationsystem.

FIG. 5 illustrates a block diagram for a realization of a subscribermonitoring system for receiving video signals;

FIG. 6 illustrates a block diagram of a channel processor;

FIG. 7 illustrates a channel sequence and volume over a twenty-four (24)hour period;

FIG. 8 illustrates a time of day detailed record;

FIG. 9 illustrates a household viewing habits statistical table;

FIG. 10A illustrates an entity-relationship diagram for the generationof program characteristics vectors;

FIG. 10B illustrates a flowchart for program characterization;

FIG. 11A illustrates a deterministic program category vector;

FIG. 11B illustrates a deterministic program sub-category vector;

FIG. 11C illustrates a deterministic program rating vector;

FIG. 11D illustrates a probabilistic program category vector;

FIG. 11E illustrates a probabilistic program sub-category vector;

FIG. 11F illustrates a probabilistic program content vector;

FIG. 12A illustrates a set of logical heuristic rules;

FIG. 12B illustrates a set of heuristic rules expressed in terms ofconditional probabilities;

FIG. 13 illustrates an entity-relationship diagram for the generation ofprogram demographic vectors;

FIG. 14 illustrates a program demographic vector;

FIG. 15 illustrates an entity-relationship diagram for the generation ofhousehold session demographic data and household session interestprofiles;

FIG. 16 illustrates an entity-relationship diagram for the generation ofaverage and session household demographic characteristics;

FIG. 17 illustrates average and session household demographic data;

FIG. 18 illustrates an entity-relationship diagram for generation of ahousehold interest profile;

FIG. 19 illustrates a household interest profile including programmingand product profiles;

FIGS. 20A-B illustrate user relationship diagrams for the presentinvention;

FIGS. 21A-D illustrate a probabilistic consumer demographiccharacterization vector, a deterministic consumer demographiccharacterization vector, a consumer product preference characterizationvector, and a storage structure for consumer characterization vectorsrespectively;

FIGS. 22A-B illustrate an advertisement demographic characterizationvector and an advertisement product preference characterization vectorrespectively;

FIG. 23 illustrates a context diagram for the present invention;

FIGS. 24A-B illustrate pseudocoele updating the characteristics vectorsand for a correlation operation respectively;

FIG. 25 illustrates heuristic rules;

FIGS. 26A-B illustrate flowcharts for updating consumer characterizationvectors and a correlation operation respectively;

FIG. 27 represents pricing as a function of correlation;

FIG. 28 illustrates a representation of a consumer characterization as aset of basis vectors and an ad characterization vector;

FIG. 29 illustrates an exemplary implementation of distributeddatabases, each of which contain a portion of information that can beutilized to create a subscriber/consumer profile; and

FIGS. 30A-B illustrate examples of demographic factors includinghousehold size and ethnicity.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In describing a preferred embodiment of the invention illustrated in thedrawings, specific terminology will be used for the sake of clarity.However, the invention is not intended to be limited to the specificterms so selected, and it is to be understood that each specific termincludes all technical equivalents which operate in a similar manner toaccomplish a similar purpose.

With reference to the drawings, in general, and FIGS. 1A through 30B inparticular, the apparatus of the present invention is disclosed.

The principles of the present invention propose a method and system fortargeting advertisements to only a selected group of subscribers withoutjeopardizing the privacy of the subscribers. As illustrated in FIG. 1A,advertisement applicability, in accordance with the principles of thepresent invention may be modeled as a distribution curve. As illustratedin FIG. 1A, a well-designed advertisement may be found to be“applicable” by the majority of subscribers, but there will be a numberof subscribers for whom the advertisement will not be applicable.Similarly, some of the subscribers may find the advertisement to bequite applicable or extremely applicable. The subscribers that find theadvertisement to be extremely applicable are most likely to purchase theproduct or service, and the subscribers that find the advertisement tobe less applicable are less likely to purchase the product or service.

Thus, in accordance with the principles of the present invention, theoverall subscribership may be divided into subgroups (smaller groups),and the advertisement may be displayed only to the subgroup that is mostinterested in the advertisement and is most likely to purchase theproduct. FIG. 1B illustrates an exemplary case where subscribers aredivided into subgroups, and the advertisement is displayed only to asubgroup of the subscribers.

By forming subgroups and targeting advertisements to one or moresubgroups, the effectiveness of the advertisements may be greatlyincreased, and overall advertisement success rates may be increased. Theincrease in overall advertisement success rates represents moreeffective use of advertising dollars, and is a “welfare gain” in thesense that those dollars may be used for other goods and services. FIG.1C illustrates an exemplary case where different success rates aredetermined by measuring products or services that were purchased as theresult of the viewing of an advertisement. As can be seen, the highestsuccess rate corresponds to the subgroup that finds the advertisement tobe extremely applicable, and the lowest success rate corresponds to thesubgroup that finds the advertisement least applicable.

The principles of the present invention may be applied to many differentapplications. In one embodiment, the present invention is utilized in acable-based television (CTV) system. FIG. 2 illustrates an exemplary CTVsystem based on a traditional advertising business model. The CTV systemconsists of a content provider 203 (e.g., programmers) producingsyndicated programs having advertising spots (avails). The contentprovider 203 also incorporates national advertisements that are receivedfrom a national advertiser 205. The programming contents (along withnational advertisements) are then provided to a network operator (e.g.,cable operator) 207. Generally, the network operator 207 purchases theprogramming contents for a fee. The network provider 207 is alsoprovided with a right to substitute a percentage of the nationaladvertisements with local advertisements (e.g. 20% of the advertisementsmay be substituted).

Thus, the network operator 207 may directly receive from one or morelocal advertisers 209 local advertisements to replace a percentage ofthe national advertisements. The local advertisements may also bereceived from the national advertiser 205. The network operator 207 thendelivers the advertisements and programming to subscribers/consumer 215via an access network 211. The information may be delivered to apersonal computer or a television or any other display means at thesubscriber end. The access network 211 may be a cable-based system, asatellite-based television system, an Internet-based computer network,or a Switched Digital Video (SDV) platform using xDSL transmissiontechnology. Such access systems are well known to those skilled in theart.

In traditional systems, e.g., in the exemplary system of FIG. 2, thelocal advertisements are not generally customized based on theneeds/preferences of the subscribers. Instead, the local advertisementsare selected based on local markets, and the same advertisement isdisplayed to a subgroup, e.g., the opening of a local store may beadvertised to a few local subscribers. Thus, even though the traditionaladvertising scheme as illustrated in FIG. 2 attempts to substitutenational/generic advertisements with some local advertisements, theeffectiveness of the advertisements is not increased because theadvertisements are not customized/tailored based on userpreferences/likes.

FIG. 3 illustrates a system utilizing targeted advertisements based onthe principles of the present invention. In this model, the localadvertisements are delivered from the advertisers to a centralizedSecure Correlation Server™ 305 configured to perform matching of theadvertisements to subscribers or groups of subscribers. At thecorrelation server 305, the input is received from a secure profilingsystem 307 in the form of subscriber profiles, and advertisements arematched to one or more subscriber profiles.

As illustrated in FIG. 3, a content provider 303 receives nationaladvertisements from one or more advertisers 301, multiplexes thenational advertisements in the programming and forwards the programstreams having national advertisements to the correlation server 305.The correlation server 305 evaluates the advertisements and attempts tomatch them with one or more subscriber profiles stored in the secureprofiling system 307. The correlation server 305, based on one or moresubscriber profiles can substitute national advertisements within theprogram streams with more targeted advertisements received from localadvertisers 309 or from national advertisers 311. The correlation server305 may also receive local advertisements from the advertisers 301.

The correlation server 305 forwards programming having targetedadvertisements to a network operator 313. The programming havingtargeted advertisements may then be forwarded to a subscriber 317 via anaccess network 315. On the subscriber end, the information may bedelivered to a personal computer or a television or any other displaymeans.

FIG. 3 illustrates the ability of a system in accordance with theprinciples of the present invention to target national advertisements aswell as local advertisements. The advertisers may provide nationaladvertisements to a Secure Correlation Server™ 305 that may match theadvertisements to different subscribers 317. By providing the ability tomatch advertisements to demographic groups (in cable television systems)and to individual subscribers (in switched digital video systems) usingthe correlation process, the present invention allows for substantialincreases in advertising effectiveness.

The system of FIG. 3 is secure for many reasons. First, the correlationserver 305 does not contain raw data such as viewing or purchaserecords. Second, the correlation server 305 does not transmitsubscriber/consumer profiles to third parties, and only performsinternal calculations to determine the applicability of an advertisementto an individual subscriber.

It is to be noted that even though previously described embodiments aredescribed with reference to video advertisements, the principles of thepresent invention are not based on a particular media. The principles ofthe present invention may be applied to diverse media such as printedmedia in which there are national (broadcast) advertisements as well aslocal advertisements, Internet advertisements, radio advertisements (inparticular Internet radio broadcasting) and a variety of other forms ofmedia advertisements.

In accordance with the principles of the present invention, differenttypes of profiles may be created by the secure profiling system 307.These profiles may be subscriber profiles created from video selectiondata, consumer profiles created from retail purchases, and profilescreated from the voluntary information provided by theconsumer/subscriber. In a switched digital video system, these profilesmay be based on individual viewing habits. In cable-based televisionsystems, these profiles may be based on specific pay-per-view demands.In Internet-based computer networks, these profiles may be based uponInternet surfing habits.

As discussed above, one type of profile that can be generated is basedon video selection data. The programming viewed by the subscriber, bothentertainment and advertisement, can be studied and processed by asubscriber characterization system to determine program characteristics.This determination of the program characteristics is referred to as aprogram characteristics vector. The vector may be a trulyone-dimensional vector, but can also be represented as an n dimensionalmatrix that can be decomposed into vectors.

The subscriber profile vector represents a profile of the subscriber (orthe household of subscribers) and can be in the form of a demographicprofile (average or session) or a program or product preference vector.The program and product preference vectors are considered to be part ofa household interest profile that can be thought of as an n dimensionalmatrix representing probabilistic measurements of subscriber interests.

In the case that the subscriber profile vector is a demographic profile,the subscriber profile vector indicates a probabilistic measure of theage of the subscriber or average age of the viewers in the household,sex of the subscriber, income range of the subscriber or household, andother such demographic data. Such information comprises householddemographic characteristics and is composed of both average and sessionvalues. Extracting a single set of values from the household demographiccharacteristics can correspond to a subscriber profile vector.

The household interest profile can contain both programming and productprofiles, with programming profiles corresponding to probabilisticdeterminations of what programming the subscriber (household) is likelyto be interested in, and product profiles corresponding to what productsthe subscriber (household) is likely to be interested in. These profilescontain both an average value and a session value, the average valuebeing a time average of data, where the averaging period may be severaldays, weeks, months, or the time between resets of unit.

Since a viewing session is likely to be dominated by a particularviewer, the session values may, in some circumstances, correspond mostclosely to the subscriber values, while the average values may, in somecircumstances, correspond most closely to the household values.

FIG. 4 depicts the context diagram of a preferred embodiment of aSubscriber Characterization System (SCS) 400. A context diagram, incombination with entity-relationship diagrams, provide a basis fromwhich one skilled in the art can realize the present invention. Thepresent invention can be realized in a number of programming languagesincluding C, C++, Perl, and Java, although the scope of the invention isnot limited by the choice of a particular programming language or tool.Object oriented languages have several advantages in terms ofconstruction of the software used to realize the present invention,although the present invention can be realized in procedural or othertypes of programming languages known to those skilled in the art.

In generating a subscriber profile, the SCS 400 receives from a user 420commands in the form of a volume control signal 424 or program selectiondata 422 which can be in the form of a channel change but may also be anaddress request which requests the delivery of programming from anetwork address. A record signal 426 indicates that the programming orthe address of the programming is being recorded by the user. The recordsignal 426 can also be a printing command, a tape recording command, abookmark command or any other command intended to store the programbeing viewed, or program address, for later use.

The material being viewed by the user 420 is referred to as sourcematerial 430. The source material 430, as defined herein, is the contentthat a subscriber selects and may consist of analog video, MotionPicture Expert Group (MPEG) digital video source material, other digitalor analog material, Hypertext Markup Language (HTML) or other type ofmultimedia source material. The subscriber characterization system 400can access the source material 430 received by the user 420 using astart signal 432 and a stop signal 434, which control the transfer ofsource related text 436 which can be analyzed as described herein.

In a preferred embodiment, the source related text 436 can be extractedfrom the source material 430 and stored in memory. The source relatedtext 436, as defined herein, includes source related textual informationincluding descriptive fields that are related to the source material430, or text that is part of the source material 430 itself. The sourcerelated text 436 can be derived from a number of sources including butnot limited to closed captioning information, Electronic Program Guide(EPG) material, and text information in the source itself (e.g. text inHTML files).

Electronic Program Guide (EPG) 440 contains information related to thesource material 430 that is useful to the user 420. The EPG 440 istypically a navigational tool that contains source related informationincluding but not limited to the programming category, programdescription, rating, actors, and duration. The structure and content ofEPG data is described in detail in U.S. Pat. No. 5,596,373 assigned toSony Corporation and Sony Electronics that is herein incorporated byreference. As shown in FIG. 4, the EPG 440 can be accessed by the SCS400 by a request EPG data signal 442 that results in the return of acategory 444, a sub-category 446, and a program description 448.

In one embodiment of the present invention, EPG data is accessed andprogram information such as the category 444, the sub-category 446, andthe program description 448 are stored in memory.

In another embodiment of the present invention, the source related text436 is the closed captioning text embedded in the analog or digitalvideo signal. Such closed captioning text can be stored in memory forprocessing to extract the program characteristic vectors 450.

One of the functions of the SCS 400 is to generate the programcharacteristics vectors 450 which are comprised of programcharacteristics data 452, as illustrated in FIG. 4. The programcharacteristics data 452, which can be used to create the programcharacteristics vectors 450 both in vector and table form, are examplesof source related information that represent characteristics of thesource material. In a preferred embodiment, the program characteristicsvectors 450 are lists of values that characterize the programming(source) material in according to the category 444, the sub-category446, and the program description 448. The present invention may also beapplied to advertisements, in which case program characteristics vectorscontain, as an example, a product category, a product sub-category, anda brand name.

As illustrated in FIG. 4, the SCS 400 uses heuristic rules 460. Theheuristic rules 460, as described herein, are composed of both logicalheuristic rules as well as heuristic rules expressed in terms ofconditional probabilities. The heuristic rules 460 can be accessed bythe SCS 400 via a request rules signal 462 that results in the transferof a copy of rules 464 to the SCS 400.

The SCS 400 forms program demographic vectors 470 from programdemographics 472, as illustrated in FIG. 4. The program demographicvectors 470 also represent characteristics of source related informationin the form of the intended or expected demographics of the audience forwhich the source material is intended.

Subscriber selection data 410 is obtained from the monitored activitiesof the user and in a preferred embodiment can be stored in a dedicatedmemory. In an alternate embodiment, the subscriber selection data 410 isstored in a storage disk. Information that is utilized to form thesubscriber selection data 410 includes time 412, which corresponds tothe time of an event, channel ID 414, program ID 416, volume level 418,channel change record 419, and program title 417. A detailed record ofselection data is illustrated in FIG. 8.

In a preferred embodiment, a household viewing habits 495 illustrated inFIG. 4 is computed from the subscriber selection data 410. The SCS 400transfers household viewing data 497 to form household viewing habits495. The household viewing data 497 is derived from the subscriberselection data 410 by looking at viewing habits at a particular time ofday over an extended period of time, usually several days or weeks, andmaking some generalizations regarding the viewing habits during thattime period.

The program characteristics vector 450 is derived from the sourcerelated text 436 and/or from the EPG 440 by applying informationretrieval techniques. The details of this process are discussed inaccordance with FIG. 10.

The program characteristics vector 450 is used in combination with a setof the heuristic rules 460 to define a set of the program demographicvectors 470 illustrated in FIG. 4 describing the audience the program isintended for.

One output of the SCS 400 is a household profile including householddemographic characteristics 490 and a household interest profile 480.The household demographic characteristics 490 resulting from thetransfer of household demographic data 492, and the household interestprofile 480, resulting from the transfer of household interests data482. Both the household demographics characteristics 490 and thehousehold interest profile 480 have a session value and an averagevalue, as will be discussed herein.

The monitoring system depicted in FIG. 5 is responsible for monitoringthe subscriber activities, and can be used to realize the SCS 400. In apreferred embodiment, the monitoring system of FIG. 5 is located in atelevision set-top device or in the television itself. In an alternateembodiment, the monitoring system is part of a computer that receivesprogramming from a network.

In an application of the system for television services, an inputconnector 520 accepts the video signal coming either from an antenna,cable television input, or other network. The video signal can be analogor Digital MPEG. Alternatively, the video source may be a video streamor other multimedia stream from a communications network including theInternet.

In the case of either analog or digital video, selected fields aredefined to carry EPG data or closed captioning text. For analog video,the closed captioning text is embedded in the vertical blanking interval(VBI). As described in U.S. Pat. No. 5,579,005, assigned toScientific-Atlanta, Inc., the EPG information can be carried in adedicated channel or embedded in the VBI. For digital video, the closedcaptioning text is carried as video user bits in a user data field. TheEPG data is transmitted as ancillary data and is multiplexed at thetransport layer with the audio and video data.

Referring to FIG. 5, a system control unit 500 receives commands fromthe user 520, decodes the command and forwards the command to thedestined module. In a preferred embodiment, the commands are entered viaa remote control to a remote receiver 505 or a set of selection buttons507 available at the front panel of the system control unit 500. In analternate embodiment, the commands are entered by the user 420 via akeyboard.

The system control unit 500 also contains a Central Processing Unit(CPU) 503 for processing and supervising all of the operations of thesystem control unit 500, a Read Only Memory (ROM) 502 containing thesoftware and fixed data, a Random Access Memory (RAM) 504 for storingdata. CPU 503, RAM 504, ROM 502, and I/O controller 501 are attached toa master bus 506. A power supply in a form of battery can also beincluded in the system control unit 500 for backup in case of poweroutage.

An input/output (I/O) controller 501 interfaces the system control unit500 with external devices. In a preferred embodiment, the I/O controller501 interfaces to the remote receiver 505 and a selection button such asthe channel change button on a remote control. In an alternateembodiment, it can accept input from a keyboard or a mouse.

The program selection data 422 is forwarded to a channel processor 510.The channel processor 510 tunes to a selected channel and the mediastream is decomposed into its basic components: the video stream, theaudio stream, and the data stream. The video stream is directed to avideo processor module 530 where it is decoded and further processed fordisplay to the TV screen. The audio stream is directed to an audioprocessor 540 for decoding and output to the speakers.

The data stream can be EPG data, closed captioning text, Extended DataService (EDS) information, a combination of these, or an alternate typeof data. In the case of EDS the call sign, program name and other usefuldata are provided. In a preferred embodiment, the data stream is storedin a reserved location of the RAM 504. In an alternate embodiment, amagnetic disk is used for data storage. The system control unit 500writes also in a dedicated memory, which in a preferred embodiment isthe RAM 504, the selected channel, the time 412 of selection, the volumelevel 418 and the program ID 416 and the program title 417. Uponreceiving the program selection data 422, the new selected channel isdirected to the channel processor 510 and the system control unit 500writes to the dedicated memory the channel selection end time and theprogram title 417 at the time 412 of channel change. The system controlunit 500 keeps track of the number of channel changes occurring duringthe viewing time via the channel change record 419. This data forms partof the subscriber selection data 410.

The volume control signal 424 is sent to the audio processor 540. In apreferred embodiment, the volume level 418 selected by the user 420corresponds to the listening volume. In an alternate embodiment, thevolume level 418 selected by the user 420 represents a volume level toanother piece of equipment such as an audio system (home theatre system)or to the television itself. In such a case, the volume can be measureddirectly by a microphone or other audio sensing device that can monitorthe volume at which the selected source material is being listened.

A program change occurring while watching a selected channel is alsologged by the system control unit 500. Monitoring the content of theprogram at the time of the program change can be done by reading thecontent of the EDS. The EDS contains information such as program title,which is transmitted via the VBI. A change on the program title field isdetected by the monitoring system and logged as an event. In analternate embodiment, an EPG is present and program information can beextracted from the EPG. In a preferred embodiment, the programming datareceived from the EDS or EPG permits distinguishing betweenentertainment programming and advertisements.

FIG. 6 illustrates the block diagram of the channel processor 510. In apreferred embodiment, the input connector 520 connects to a tuner 600that tunes to the selected channel. A local oscillator can be used toheterodyne the signal to the IF signal. A demodulator 602 demodulatesthe received signal and the output is fed to an FEC decoder 604. Thedata stream received from the FEC decoder 604 is, in a preferredembodiment, in an MPEG format. In a preferred embodiment, systemdemultiplexer 606 separates out video and audio information forsubsequent decompression and processing, as well as ancillary data whichcan contain program related information.

The data stream presented to the system demultiplexer 606 consists ofpackets of data including video, audio and ancillary data. The systemdemultiplexer 606 identifies each packet from the stream ID and directsthe stream to the corresponding processor. The video data is directed tothe video processor module 530 and the audio data is directed to theaudio processor 540. The ancillary data can contain closed captioningtext, emergency messages, program guide, or other useful information.

Closed captioning text is considered to be ancillary data and is thuscontained in the video stream. The system demultiplexer 606 accesses theuser data field of the video stream to extract the closed captioningtext. The program guide, if present, is carried on data streamidentified by a specific transport program identifier.

In an alternate embodiment, analog video can be used. For analogprogramming, ancillary data such as closed captioning text or EDS dataare carried in a vertical blanking interval.

FIG. 7 illustrates a channel sequence and volume over a twenty-four (24)hour period. The Y-axis represents the status of the receiver in termsof on/off status and volume level. The X-axis represents the time ofday. The channels viewed are represented by the windows 701-706, with afirst channel 702 being watched followed by the viewing of a secondchannel 704, and a third channel 706 in the morning. In the evening afourth channel 701 is watched, a fifth channel 703, and a sixth channel705. A channel change is illustrated by a momentary transition to the“off” status and a volume change is represented by a change of level onthe Y-axis.

A detailed record of the subscriber selection data 410 is illustrated inFIG. 8 in a table format. A time column 802 contains the starting timeof every event occurring during the viewing time. A Channel ID column804 lists the channels viewed or visited during that period. A programtitle column 803 contains the titles of all programs viewed. A volumecolumn 801 contains the volume level 418 at the time 412 of viewing aselected channel.

A representative statistical record corresponding to the householdviewing habits 495 is illustrated in FIG. 9. In a preferred embodiment,a time of day column 900 is organized in period of time includingmorning, mid-day, afternoon, night, and late night. In an alternateembodiment, smaller time periods are used. A minutes watched column 902lists, for each period of time, the time in minutes in which the SCS 400recorded delivery of programming. The number of channel changes duringthat period and the average volume are also included in that table in achannel changes column 904 and an average volume column 906respectively. The last row of the statistical record contains the totalsfor the items listed in the minutes watched column 902, the channelchanges column 904 and the average volume 906.

FIG. 10A illustrates an entity-relationship diagram for the generationof the program characteristics vector 450. The context vector generationand retrieval technique described in U.S. Pat. No. 5,619,709, which isincorporated herein by reference, can be applied for the generation ofthe program characteristics vectors 450. Other techniques are well knownby those skilled in the art.

Referring to FIG. 10A, the source material 430 or the EPG 440 is passedthrough a program characterization process 1000 to generate the programcharacteristics vectors 450. The program characterization process 1000is described in accordance with FIG. 10B. Program content descriptorsincluding a first program content descriptor 1002, a second programcontent descriptor 1004 and an nth program content descriptor 1006, eachclassified in terms of the category 444, the sub-category 446, and otherdivisions as identified in the industry accepted program classificationsystem, are presented to a context vector generator 1020. As an example,the program content descriptor can be text representative of theexpected content of material found in the particular program category444. In this example, the program content descriptors 1002, 1004 and1006 would contain text representative of what would be found inprograms in the news, fiction, and advertising categories respectively.The context vector generator 1020 generates context vectors for that setof sample texts resulting in a first summary context vector 1008, asecond summary context vector 1010, and an nth summary context vector1012. In the example given, the summary context vectors 1008, 1010, and1012 correspond to the categories of news, fiction and advertisingrespectively. The summary vectors are stored in a local data storagesystem.

Referring to FIG. 10B, a sample of the source related text 436 that isassociated with the new program to be classified is passed to thecontext vector generator 1020 that generates a program context vector1040 for that program. The source related text 436 can be either thesource material 430, the EPG 440, or other text associated with thesource material. A comparison is made between the actual program contextvectors and the stored program content context vectors by computing, ina dot product computation process 1030, the dot product of the firstsummary context vector 1008 with the program context vector 1040 toproduce a first dot product 1014. Similar operations are performed toproduce second dot product 1016 and nth dot product 1018.

The values contained in the dot products 1014, 1016 and 1018, while notprobabilistic in nature, can be expressed in probabilistic terms using asimple transformation in which the result represents a confidence levelof assigning the corresponding content to that program. The transformedvalues add up to one. The dot products can be used to classify aprogram, or form a weighted sum of classifications that results in theprogram characteristics vectors 450. In the example given, if the sourcerelated text 436 was from an advertisement, the nth dot product 1018would have a high value, indicating that the advertising category wasthe most appropriate category, and assigning a high probability value tothat category. If the dot products corresponding to the other categorieswere significantly higher than zero, those categories would be assigneda value, with the result being the program characteristics vectors 450as shown in FIG. 11D.

For the sub-categories, probabilities obtained from the contentpertaining to the same sub-category 446 are summed to form theprobability for the new program being in that sub-category 446. At thesub-category level, the same method is applied to compute theprobability of a program being from the given category 444. The threelevels of the program classification system; the category 444, thesub-category 446 and the content, are used by the programcharacterization process 1000 to form the program characteristicsvectors 450 which are depicted in FIGS. 11D-11F.

The program characteristics vectors 450 in general are represented inFIGS. 11A-F. FIGS. 11A-C are an example of deterministic programvectors. This set of vectors is generated when the programcharacteristics are well defined, as can occur when the source relatedtext 436 or the EPG 440 contains specific fields identifying thecategory 444 and the sub-category 446. A program rating can alsoprovided by the EPG 440.

In the case that these characteristics are not specified, a statisticalset of vectors is generated from the process described in accordancewith FIG. 10. FIG. 11D shows the probability that a program beingwatched is from the given category 444. The categories are listed in theX-axis. The sub-category 446 is also expressed in terms of probability.This is shown in FIG. 11E. The content component of this set of vectorsis a third possible level of the program classification, and isillustrated in FIG. 11F.

FIG. 12A illustrates sets of logical heuristics rules that form part ofthe heuristic rules 460. In a preferred embodiment, logical heuristicrules are obtained from sociological or psychological studies. Two typesof rules are illustrated in FIG. 12A. The first type links anindividual's viewing characteristics to demographic characteristics suchas gender, age, and income level. A channel changing rate rule 1230attempts to determine gender based on channel change rate. An incomerelated channel change rate rule 1210 attempts to link channel changerates to income brackets. A second type of rules links particularprograms to particular audience, as illustrated by a gender determiningrule 1250 which links the program category 444/sub-category 446 with agender. The result of the application of the logical heuristic rulesillustrated in FIG. 12A are probabilistic determinations of factorsincluding gender, age, and income level. Although a specific set oflogical heuristic rules has been used as an example, a wide number oftypes of logical heuristic rules can be used to realize the presentinvention. In addition, these rules can be changed based on learningwithin the system or based on external studies that provide moreaccurate rules.

FIG. 12B illustrates a set of the heuristic rules 460 expressed in termsof conditional probabilities. In the example shown in FIG. 12B, thecategory 444 has associated with it conditional probabilities fordemographic factors such as age, income, family size and gendercomposition. The category 444 has associated with it conditionalprobabilities that represent probability that the viewing group iswithin a certain age group dependent on the probability that they areviewing a program in that category 444.

FIG. 13 illustrates an entity-relationship diagram for the generation ofthe program demographic vectors 470. In a preferred embodiment, theheuristic rules 460 are applied along with the program characteristicvectors 450 in a program target analysis process 1300 to form theprogram demographic vectors 470. The program characteristic vectors 450indicate a particular aspect of a program, such as its violence level.The heuristic rules 460 indicate that a particular demographic group hasa preference for that program. As an example, it may be the case thatyoung males have a higher preference for violent programs than othersectors of the population. Thus, a program which has the programcharacteristic vectors 450 indicating a high probability of havingviolent content, when combined with the heuristic rules 460 indicatingthat “young males like violent programs”, will result, through theprogram target analysis process 1300, in the program demographic vectors470 which indicate that there is a high probability that the program isbeing watched by a young male.

The program target analysis process 1300 can be realized using softwareprogrammed in a variety of languages which processes mathematically theheuristic rules 460 to derive the program demographic vectors 470. Thetable representation of the heuristic rules 460 illustrated in FIG. 12Bexpresses the probability that the individual or household is from aspecific demographic group based on a program with a particular category444. This can be expressed, using probability terms as follow “theprobability that the individuals are in a given demographic groupconditional to the program being in a given category”. Referring to FIG.14, the probability that the group has certain demographiccharacteristics based on the program being in a specific category isillustrated.

Expressing the probability that a program is destined to a specificdemographic group can be determined by applying Bayes rule. Thisprobability is the sum of the conditional probabilities that thedemographic group likes the program, conditional to the category 444weighted by the probability that the program is from that category 444.In a preferred embodiment, the program target analysis can calculate theprogram demographic vectors by application of logical heuristic rules,as illustrated in FIG. 12A, and by application of heuristic rulesexpressed as conditional probabilities as shown in FIG. 12B. Logicalheuristic rules can be applied using logical programming and fuzzy logicusing techniques well understood by those skilled in the art, and arediscussed in the text by S. V. Kartalopoulos entitled “UnderstandingNeural Networks and Fuzzy Logic” which is incorporated herein byreference.

Conditional probabilities can be applied by simple mathematicaloperations multiplying program context vectors by matrices ofconditional probabilities. By performing this process over all thedemographic groups, the program target analysis process 1300 can measurehow likely a program is to be of interest to each demographic group.Those probabilities values form the program demographic vector 470represented in FIG. 14.

As an example, the heuristic rules expressed as conditionalprobabilities shown in FIG. 12B are used as part of a matrixmultiplication in which the program characteristics vector 450 ofdimension N, such as those shown in FIGS. 11A-11F is multiplied by anN×M matrix of heuristic rules expressed as conditional probabilities,such as that shown in FIG. 12B. The resulting vector of dimension M is aweighted average of the conditional probabilities for each category andrepresents the household demographic characteristics 490. Similarprocessing can be performed at the sub-category and content levels.

FIG. 14 illustrates an example of the program demographic vector 470,and shows the extent to which a particular program is destined to aparticular audience. This is measured in terms of probability asdepicted in FIG. 14. The Y-axis is the probability of appealing to thedemographic group identified on the X-axis.

FIG. 15 illustrates an entity-relationship diagram for the generation ofhousehold session demographic data 1510 and household session interestprofile 1520. In a preferred embodiment, the subscriber selection data410 is used along with the program characteristics vectors 450 in asession characterization process 1500 to generate the household sessioninterest profile 1520. The subscriber selection data 410 indicates whatthe subscriber is watching, for how long and at what volume they arewatching the program.

In a preferred embodiment, the session characterization process 1500forms a weighted average of the program characteristics vectors 450 inwhich the time duration the program is watched is normalized to thesession time (typically defined as the time from which the unit wasturned on to the present). The program characteristics vectors 450 aremultiplied by the normalized time duration (which is less than oneunless only one program has been viewed) and summed with the previousvalue. Time duration data, along with other subscriber viewinginformation, is available from the subscriber selection data 410. Theresulting weighted average of program characteristics vectors forms thehousehold session interest profile 1520, with each program contributingto the household session interest profile 1520 according to how long itwas watched. The household session interest profile 1520 is normalizedto produce probabilistic values of the household programming interestsduring that session.

In an alternate embodiment, the heuristic rules 460 are applied to boththe subscriber selection data 410 and the program characteristicsvectors 450 to generate the household session demographic data 1510 andthe household session interest profile 1520. In this embodiment,weighted averages of the program characteristics vectors 450 are formedbased on the subscriber selection data 410, and the heuristic rules 460are applied. In the case of logical heuristic rules as shown in FIG.12A, logical programming can be applied to make determinations regardingthe household session demographic data 1510 and the household sessioninterest profile 1520. In the case of heuristic rules in the form ofconditional probabilities such as those illustrated in FIG. 12B, a dotproduct of the time averaged values of the program characteristicsvectors can be taken with the appropriate matrix of heuristic rules togenerate both the household session demographic data 1510 and thehousehold session interest profile 1520.

Volume control measurements which form part of the subscriber selectiondata 410 can also be applied in the session characterization process1500 to form a household session interest profile 1520. This can beaccomplished by using normalized volume measurements in a weightedaverage manner similar to how time duration is used. Thus, muting a showresults in a zero value for volume, and the program characteristicsvector 450 for this show will not be averaged into the household sessioninterest profile 1520.

FIG. 16 illustrates an entity-relationship diagram for the generation ofaverage household demographic characteristics and session householddemographic characteristics 490. A household demographiccharacterization process 1600 generates the household demographiccharacteristics 490 represented in table format in FIG. 17. Thehousehold demographic characterization process 1600 uses the householdviewing habits 495 in combination with the heuristic rules 460 todetermine demographic data. For example, a household with a number ofminutes watched of zero during the day may indicate a household with twoworking adults. Both logical heuristic rules as well as rules based onconditional probabilities can be applied to the household viewing habits495 to obtain the household demographics characteristics 490.

The household viewing habits 495 is also used by the system to detectout-of-habits events. For example, if a household with a zero value forthe minutes watched column 902 at late night presents a session value atthat time via the household session demographic data 1510, this sessionwill be characterized as an out-of-habits event and the system canexclude such data from the average if it is highly probable that thedemographics for that session are greatly different than the averagedemographics for the household. Nevertheless, the results of theapplication of the household demographic characterization process 1600to the household session demographic data 1510 can result in valuablesession demographic data, even if such data is not added to the averagedemographic characterization of the household.

FIG. 17 illustrates the average and session household demographiccharacteristics. A household demographic parameters column 1701 isfollowed by an average value column 1705, a session value column 1703,and an update column 1707. The average value column 1705 and the sessionvalue column 1703 are derived from the household demographiccharacterization process 1600. The deterministic parameters such asaddress and telephone numbers can be obtained from an outside source orcan be loaded into the system by the subscriber or a network operator atthe time of installation. Updating of deterministic values is preventedby indicating that these values should not be updated in the updatecolumn 1707.

FIG. 18 illustrates an entity-relationship diagram for the generation ofthe household interest profile 480 in a household interest profilegeneration process 1800. In a preferred embodiment, the householdinterest profile generation process comprises averaging the householdsession interest profile 1520 over multiple sessions and applying thehousehold viewing habits 495 in combination with the heuristic rules 460to form the household interest profile 480 which takes into account boththe viewing preferences of the household as well as assumptions abouthouseholds/subscribers with those viewing habits and programpreferences.

FIG. 19 illustrates the household interest profile 480 that is composedof a programming types row 1909, a products types row 1907, and ahousehold interests column 1901, an average value column 1903, and asession value column 1905.

The product types row 1907 gives an indication as to what type ofadvertisement the household would be interested in watching, thusindicating what types of products could potentially be advertised with ahigh probability of the advertisement being watched in its entirety. Theprogramming types row 1909 suggests what kind of programming thehousehold is likely to be interested in watching. The householdinterests column 1901 specifies the types of programming and productsthat are statistically characterized for that household.

As an example of the industrial applicability of the invention, ahousehold will perform its normal viewing routine without beingrequested to answer specific questions regarding likes and dislikes.Children may watch television in the morning in the household, and maychange channels during commercials, or not at all. The television mayremain off during the working day, while the children are at school andday care, and be turned on again in the evening, at which time theparents may “surf” channels, mute the television during commercials, andultimately watch one or two hours of broadcast programming. The presentinvention provides the ability to characterize the household, and maymake the determination that there are children and adults in thehousehold, with program and product interests indicated in the householdinterest profile 480 corresponding to a family of that composition. Ahousehold with two retired adults will have a completely differentcharacterization that will be indicated in the household interestprofile 480.

As discussed above, an additional method of profiling includes profilingbased on consumer purchases. FIG. 20A shows a user relationship diagramthat illustrates the relationships between a consumer profiling systemand various entities. As can be seen in FIG. 20A, a consumer 2000 canreceive information and advertisements from a consumer personal computer(PC) 2004, displayed on a television 2008 which is connected to aset-top 2006, or can receive a mailed ad 2082.

Advertisements and information displayed on consumer PC 2004 ortelevision 2008 can be received over an Internet 2050, or can bereceived over the combination of the Internet 2050 with anothertelecommunications access system. The telecommunications access systemcan include but is not limited to cable TV delivery systems, switcheddigital video access systems operating over telephone wires, microwavetelecommunications systems, or any other medium which providesconnectivity between the consumer 2000 and a content server 2062 and adserver 2046.

A content/opportunity provider 2060 maintains the content server 2062which can transmit content including broadcast programming across anetwork such as the Internet 2050. Other methods of data transport canbe used including private data networks and can connect the contentsever 2060 through an access system to a device owned by consumer 2000.

Content/opportunity provider 2060 is termed such since if consumer 2000is receiving a transmission from content server 2062, thecontent/opportunity provider can insert an advertisement. For videoprogramming, content/opportunity provider is typically the cable networkoperator or the source of entertainment material, and the opportunity isthe ability to transmit an advertisement during a commercial break.

The majority of content that is being transmitted today is done so inbroadcast form, such as broadcast television programming (broadcast overthe air and via cable TV networks), broadcast radio, and newspapers.Although the interconnectivity provided by the Internet will allowconsumer specific programming to be transmitted, there will still be alarge amount of broadcast material that can be sponsored in part byadvertising. The ability to insert an advertisement in a broadcaststream (video, audio, or mailed) is an opportunity for advertiser 2044.Content can also be broadcast over the Internet and combined withexisting video services, in which case opportunities for the insertionof advertisements will be present.

Although FIG. 20A represents content/opportunity provider 2060 andcontent server 2062 as being independently connected to Internet 2050,with the consumer's devices being also being directly connected to theInternet 2050, the content/opportunity provider 2060 can also controlaccess to the subscriber. This can occur when the content/opportunityprovider is also the cable operator or telephone company. In suchinstances, the cable operator or telephone company can be providingcontent to consumer 2000 over the cable operator/telephone companyaccess network. As an example, if the cable operator has control overthe content being transmitted to the consumer 2000, and has programmedtimes for the insertion of advertisements, the cable operator isconsidered to be a content/opportunity provider 2060 since the cableoperator can provide advertisers the opportunity to access consumer 2000by inserting an advertisement at the commercial break.

In a preferred embodiment of the present invention, a pricing policy canbe defined. The content/opportunity provider 2060 can charge advertiser2044 for access to consumer 2000 during an opportunity. In a preferredembodiment the price charged for access to consumer 2000 bycontent/opportunity provider varies as a function of the applicabilityof the advertisement to consumer 2000. In an alternate embodimentconsumer 2000 retains control of access to the profile and charges forviewing an advertisement.

The content provider can also be a mailing company or printer that ispreparing printed information for consumer 2000. As an example, contentserver 2062 can be connected to a printer 2064 that creates a mailed ad2082 for consumer 2000. Alternatively, printer 2064 can produceadvertisements for insertion into newspapers that are delivered toconsumer 2000. Other printed material can be generated by printer 2062and delivered to consumer 2000 in a variety of ways.

Advertiser 2044 maintains an ad server 2046 that contains a variety ofadvertisements in the form of still video that can be printed, videoadvertisements, audio advertisements, or combinations thereof.

Profiler 2040 maintains a consumer profile server 2030 that contains thecharacterization of consumer 2000. The consumer profiling system isoperated by profiler 2040, who can use consumer profile server 2030 oranother computing device connected to consumer profile server 2030 toprofile consumer 2000.

Data to perform the consumer profiling is received from a point ofpurchase 2010. Point of purchase 2010 can be a grocery store, departmentstore, other retail outlet, or can be a web site or other location wherea purchase request is received and processed. In a preferred embodiment,data from the point of purchase is transferred over a public or privatenetwork 2020, such as a local area network within a store or a wide areanetwork that connects a number of department or grocery stores. In analternate embodiment the data from point of purchase 2010 is transmittedover the Internet 2050 to profiler 2040.

Profiler 2040 may be a retailer who collects data from its stores, butcan also be a third party who contracts with consumer 2000 and theretailer to receive point of purchase data and profile consumer 2000.Consumer 2000 may agree to such an arrangement based on the increasedconvenience offered by targeted ads, or through a compensationarrangement in which they are paid on a periodic basis for revealingtheir specific purchase records.

FIG. 20B illustrates an alternate embodiment of the present invention inwhich the consumer 2000 is also profiler 2040. Consumer 2000 maintainsconsumer profile server 2030 that is connected to a network, eitherdirectly or through consumer PC 2004 or set-top 2006. Consumer profileserver 2030 can contain the consumer profiling system, or the profilingcan be performed in conjunction with consumer PC 2004 or set-top 2006. Asubscriber characterization system that monitors the viewing habits ofconsumer 2000 can be used in conjunction with the consumer profilingsystem to create a more accurate consumer profile.

When the consumer 2000 is also the profiler 2040, as shown in FIG. 20B,access to the consumer demographic and product preferencecharacterization is controlled exclusively by consumer 2000, who willgrant access to the profile in return for receiving an increasedaccuracy of ads, for cash compensation, or in return for discounts orcoupons on goods and services.

FIG. 21A illustrates an example of a probabilistic demographiccharacterization vector. The demographic characterization vector is arepresentation of the probability that a consumer falls within a certaindemographic category such as an age group, gender, household size, orincome range.

In a preferred embodiment the demographic characterization vectorincludes interest categories. The interest categories may be organizedaccording to broad areas such as music, travel, and restaurants.Examples of music interest categories include country music, rock,classical, and folk. Examples of travel categories include “travels toanother state more than twice a year,” and travels by plane more thantwice a year.”

FIG. 21B illustrates a deterministic demographic characterizationvector. The deterministic demographic characterization vector is arepresentation of the consumer profile as determined from deterministicrather than probabilistic data. As an example, if consumer 2000 agreesto answer specific questions regarding age, gender, household size,income, and interests the data contained in the consumercharacterization vector will be deterministic.

As with probabilistic demographic characterization vectors, thedeterministic demographic characterization vector can include interestcategories. In a preferred embodiment, consumer 2000 answers specificquestions in a survey generated by profiler 2040 and administered overthe phone, in written form, or via the Internet 2050 and consumer PC2004. The survey questions correspond either directly to the elements inthe probabilistic demographic characterization vector, or can beprocessed to obtain the deterministic results for storage in thedemographic characterization vector.

FIG. 21C illustrates a product preference vector. The product preferencerepresents the average of the consumer preferences over past purchases.As an example, a consumer who buys the breakfast cereal manufactured byPost under the trademark ALPHABITS about twice as often as purchasingthe breakfast cereal manufactured by Kellogg under the trademark CORNFLAKES, but who never purchases breakfast cereal manufactured by GeneralMills under the trademark WHEATIES, would have a product preferencecharacterization such as that illustrated in FIG. 21C. As illustrated inFIG. 21C, the preferred size of the consumer purchase of a particularproduct type can also be represented in the product preference vector.

FIG. 21D represents a data structure for storing the consumer profile,which can be comprised of a consumer ID field 2137, a deterministicdemographic data field 2139, a probabilistic demographic data field2141, and one or more product preference data fields 2143. Asillustrated in FIG. 21D, the product preference data field 2143 can becomprised of multiple fields arranged by product categories 2153.

Depending on the data structure used to store the information containedin the vector, any of the previously mentioned vectors may be in thefoam of a table, record, linked tables in a relational database, seriesof records, or a software object.

A consumer ID 2312 (described later with respect to FIG. 23) can be anyidentification value uniquely associated with consumer 2000. In apreferred embodiment consumer ID 2312 is a telephone number, while in analternate embodiment consumer ID 2312 is a credit card number. Otherunique identifiers include consumer name with middle initial or a uniquealphanumeric sequence, the consumer address, social security number.

The vectors described and represented in FIGS. 21A-C form consumercharacterization vectors that can be of varying length and dimension,and portions of the characterization vector can be used individually.Vectors can also be concatenated or summed to produce longer vectorsthat provide a more detailed profile of consumer 2000. A matrixrepresentation of the vectors can be used, in which specific elements,such a product categories 2153, are indexed. Hierarchical structures canbe employed to organize the vectors and to allow hierarchical searchalgorithms to be used to locate specific portions of vectors.

FIGS. 22A-B represent an ad demographics vector and an ad productpreference vector respectively. The ad demographics vector, similar instructure to the demographic characterization vector, is used to targetthe ad by setting the demographic parameters in the ad demographicsvector to correspond to the targeted demographic group. As an example,if an advertisement is developed for a market which is the 18-24 and24-32 age brackets, no gender bias, with a typical household size of2-5, and income typically in the range of $20,000-$50,000, the addemographics vector would resemble the one shown in FIG. 22A. The addemographics vector represents a statistical estimate of who the ad isintended for, based on the advertisers belief that the ad will bebeneficial to the manufacturer when viewed by individuals in thosegroups. The benefit will typically be in the form of increased sales ofa product or increased brand recognition. As an example, an “image ad”which simply shows an artistic composition but which does not directlysell a product may be very effective for young people, but may beannoying to older individuals. The ad demographics vector can be used toestablish the criteria that will direct the ad to the demographic groupof 18-24 year olds.

FIG. 22B illustrates an ad product preference vector. The ad productpreference vector is used to select consumers that have a particularproduct preference. In the example illustrated in FIG. 22B, the adproduct preference vector is set so that the ad can be directed apurchasers of ALPHABITS and WHEATIES, but not at purchasers of CORNFLAKES. This particular setting would be useful when the advertiserrepresents Kellogg and is charged with increasing sales of CORN FLAKES.By targeting present purchasers of ALPHABITS and WHEATIES, theadvertiser can attempt to sway those purchasers over to the Kelloggbrand and in particular convince them to purchase CORN FLAKES. Giventhat there will be a payment required to present the advertisement, inthe form of a payment to the content/opportunity provider 2060 or to theconsumer 2000, the advertiser 2044 desires to target the ad and therebyincrease its cost effectiveness.

In the event that advertiser 2044 wants to reach only the purchasers ofKellogg's CORN FLAKES, that category would be set at a high value, andin the example shown would be set to 1. As shown in FIG. 22B, productsize can also be specified. If there is no preference to size categorythe values can all be set to be equal. In a preferred embodiment thevalues of each characteristic including brand and size are individuallynormalized.

Because advertisements can be targeted based on a set of demographic andproduct preference considerations which may not be representative of anyparticular group of present consumers of the product, the adcharacterization vector can be set to identify a number of demographicgroups which would normally be considered to be uncorrelated. Becausethe ad characterization vector can have target profiles which are notrepresentative of actual consumers of the product, the adcharacterization vector can be considered to have discretionaryelements. When used herein the term discretionary refers to a selectionof target market characteristics which need not be representative of anactual existing market or single purchasing segment.

In a preferred embodiment the consumer characterization vectors shown inFIGS. 21A-C and the ad characterization vectors represented in FIGS.22A-B have a standardized format, in which each demographiccharacteristic and product preference is identified by an indexedposition. In a preferred embodiment the vectors are singly indexed andthus represent coordinates in n-dimensional space, with each dimensionrepresenting a demographic or product preference characteristic. In thisembodiment a single value represents one probabilistic or deterministicvalue (e.g. the probability that the consumer is in the 18-24 year oldage group, or the weighting of an advertisement to the age group).

In an alternate embodiment a group of demographic or productcharacteristics forms an individual vector. As an example, agecategories can be considered a vector, with each component of the vectorrepresenting the probability that the consumer is in that age group. Inthis embodiment each vector can be considered to be a basis vector forthe description of the consumer or the target ad. The consumer or adcharacterization is comprised of a finite set of vectors in a vectorspace that describes the consumer or advertisement.

FIG. 23 shows a context diagram for the present invention. Contextdiagrams are useful in illustrating the relationship between a systemand external entities. Context diagrams can be especially useful indeveloping object oriented implementations of a system, although use ofa context diagram does not limit implementation of the present inventionto any particular programming language. The present invention can berealized in a variety of programming languages including but not limitedto C, C++, Smalltalk, Java, Perl, and can be developed as part of arelational database. Other languages and data structures can be utilizedto realize the present invention and are known to those skilled in theart.

Referring to FIG. 23, in a preferred embodiment consumer profilingsystem 2300 is resident on consumer profile server 2030. Point ofpurchase records 2310 are transmitted from point of purchase 2010 andstored on consumer profile server 2030. Heuristic rules 2330, pricingpolicy 2370, and consumer profile 2360 are similarly stored on consumerprofile server 130. In a preferred embodiment advertisement records 2340are stored on ad server 2046 and connectivity between advertisementrecords 2340 and consumer profiling system 2300 is via the Internet orother network.

In an alternate embodiment the entities represented in FIG. 23 arelocated on servers that are interconnected via the Internet or othernetwork.

Consumer profiling system 2300 receives purchase information from apoint of purchase, as represented by point of purchase records 2310. Theinformation contained within the point of purchase records 2310 includesthe consumer ID 2312, a product ID 2314 of the purchased product, thequantity 2316 purchased and the price 2318 of the product. In apreferred embodiment, the date and time of purchase 2320 are transmittedby point of purchase records 2310 to consumer profiling system 2300.

The consumer profiling system 2300 can access the consumer profile 2360to update the profiles contained in it. Consumer profiling system 2300retrieves a consumer characterization vector 2362 and a productpreference vector 2364. Subsequent to retrieval one or more dataprocessing algorithms are applied to update the vectors. An algorithmfor updating is illustrated in the flowchart in FIG. 26A. The updatedvectors termed herein as new demographic characterization vector 2366and new product preference 2368 are returned to consumer profile 2360for storage.

Consumer profiling system 2300 can determine probabilistic consumerdemographic characteristics based on product purchases by applyingheuristic rules 2319. Consumer profiling system 2300 provides a productID 2314 to heuristic rules records 2330 and receives heuristic rulesassociated with that product. Examples of heuristic rules areillustrated in FIG. 25.

In a preferred embodiment of the present invention, consumer profilingsystem 2300 can determine the applicability of an advertisement to theconsumer 2000. For determination of the applicability of anadvertisement, a correlation request 2346 is received by consumerprofiling system 2300 from advertisements records 2340, along withconsumer ID 2312. Advertisements records 2340 also providesadvertisement characteristics including an ad demographic vector 2348,an ad product category 2352 and an ad product preference vector 2354.

Application of a correlation process, as will be described in accordancewith FIG. 26B, results in a demographic correlation 2356 and a productcorrelation 2358 which can be returned to advertisement records 2340. Ina preferred embodiment, advertiser 2044 uses product correlation 2358and demographic correlation 2356 to determine the applicability of theadvertisement and to determine if it is worth purchasing theopportunity. In a preferred embodiment, pricing policy 2370 is utilizedto determine an ad price 2372 which can be transmitted from consumerprofiling system 2300 to advertisement records 2340 for use byadvertiser 2044.

Pricing policy 2370 is accessed by consumer profiling system 2300 toobtain ad price 2372. Pricing policy 2370 takes into considerationresults of the correlation provided by the consumer profiling system2300. An example of pricing schemes will be discussed in detail laterwith respect to FIG. 27.

FIGS. 24A and 24B illustrate pseudocode for the updating process and fora correlation operation respectively. The updating process involvesutilizing purchase information in conjunction with heuristic rules toobtain a more accurate representation of consumer 2000, stored in theform of a new demographic characterization vector 2362 and a new productpreference vector 2368.

As illustrated in the pseudocode in FIG. 24A the point of purchase dataare read and the products purchase are integrated into the updatingprocess. Consumer profiling system 2300 retrieves a product demographicsvector obtained from the set of heuristic rules 2319 and applies theproduct demographics vector to the demographics characterization vector2362 and the product preference vector 2364 from the consumer profile2360.

The updating process as illustrated by the pseudocode in FIG. 24Autilizes a weighting factor that determines the importance of thatproduct purchase with respect to all of the products purchased in aparticular product category. In a preferred embodiment the weight iscomputed as the ratio of the total of products with a particular productID 2314 purchased at that time, to the product total purchase, which isthe total quantity of the product identified by its product ID 2314purchased by consumer 2000 identified by its consumer ID 2312, purchasedover an extended period of time. In a preferred embodiment the extendedperiod of time is one year.

In the preferred embodiment the product category total purchase isdetermined from a record containing the number of times that consumer2000 has purchased a product identified by a particular product ID.

In an alternate embodiment other types of weighting factors, runningaverages and statistical filtering techniques can be used to use thepurchase data to update the demographic characterization vector. Thesystem can also be reset to clear previous demographic characterizationvectors and product preference vectors.

The new demographic characterization vector 2366 is obtained as theweighted sum of the product demographics vector and the demographiccharacterization vector 2362. The same procedure is performed to obtainthe new product preference vector 2368. Before storing those newvectors, a normalization is performed on the said new vectors. When usedherein the term product characterization information refers productdemographics vectors, product purchase vectors or heuristic rules, allof which can be used in the updating process. The product purchasevector refers to the vector that represents the purchase of an itemrepresented by a product ID. As an example, a product purchase vectorfor the purchase of Kellogg's CORN FLAKES in a 32 oz. size has a productpurchase vector with a unity value for Kellogg's CORN FLAKES and in the32 oz. size. In the updating process the weighted sum of the purchase asrepresented by the product purchase vector is added to the productpreference vector to update the product preference vector, increasingthe estimated probability that the consumer will purchase Kellogg's CORNFLAKES in the 32 oz. size.

In FIG. 24B the pseudocode for a correlation process is illustrated.Consumer profiling system 2300, after receiving the productcharacteristics and the consumer ID 2312 from the advertisement recordsretrieves the consumer demographic characterization vector 2362 and itsproduct preference vector 2364. The demographic correlation is thecorrelation between the demographic characterization vector 2362 and thead demographics vector. The product correlation is the correlationbetween the ad product preference vector 2354 and the product preferencevector 2364.

In a preferred embodiment the correlation process involves computing thedot product between vectors. The resulting scalar is the correlationbetween the two vectors.

In an alternate embodiment, as illustrated in FIG. 28, the basis vectorswhich describe aspects of the consumer can be used to calculate theprojections of the ad vector on those basis vectors. In this embodiment,the result of the ad correlation can itself be in vector form whosecomponents represent the degree of correlation of the advertisement witheach consumer demographic or product preference feature. As shown inFIG. 28 the basis vectors are the age of the consumer 2821, the incomeof the consumer 2801, and the family size of the consumer 2831. The adcharacterization vector 2850 represents the desired characteristics ofthe target audience, and can include product preference as well asdemographic characteristics.

In this embodiment the degree of orthogonality of the basis vectors willdetermine the uniqueness of the answer. The projections on the basisvectors form a set of data that represent the corresponding values forthe parameter measured in the basis vector. As an example, if householdincome is one basis vector, the projection of the ad characterizationvector on the household income basis vector will return a resultindicative of the target household income for that advertisement.

Because basis vectors cannot be readily created from some productpreference categories (e.g. cereal preferences) an alternaterepresentation to that illustrated in FIG. 21C can be utilized in whichthe product preference vector represents the statistical average ofpurchases of cereal in increasing size containers. This vector can beinterpreted as an average measure of the cereal purchased by theconsumer in a given time period.

The individual measurements of correlation as represented by thecorrelation vector can be utilized in determining the applicability ofthe advertisement to the subscriber, or a sum of correlations can begenerated to represent the overall applicability of the advertisement.

In a preferred embodiment individual measurements of the correlations,or projections of the ad characteristics vector on the consumer basisvectors, are not made available to protect consumer privacy, and onlythe absolute sum is reported. In geometric terms this can be interpretedas disclosure of the sum of the lengths of the projections rather thanthe actual projections themselves.

In an alternate embodiment the demographic and product preferenceparameters are grouped to form sets of paired scores in which elementsin the consumer characterization vector are paired with correspondingelements of the ad characteristics vector. A correlation coefficientsuch as the Pearson product-moment correlation can be calculated. Othermethods for correlation can be employed and are well known to thoseskilled in the art.

When the consumer characterization vector and the ad characterizationvector are not in a standardized format, a transformation can beperformed to standardize the order of the demographic and productpreferences, or the data can be decomposed into sets of basis vectorswhich indicate particular attributes such as age, income or family size.

FIG. 25 illustrates an example of heuristic rules including rules fordefining a product demographics vector. From the productcharacteristics, a probabilistic determination of household demographicscan be generated. Similarly, the monthly quantity purchased can be usedto estimate household size. The heuristic rules illustrated in FIG. 25serve as an example of the types of heuristic rules that can be employedto better characterize consumer 2000 as a result of their purchases. Theheuristic rules can include any set of logic tests, statisticalestimates, or market studies that provide the basis for betterestimating the demographics of consumer 2000 based on their purchases.

In FIG. 26A the flowchart for updating the consumer characterizationvectors is depicted. The system receives data from the point of purchaseat receive point of purchase information step 2600. The system performsa test to determine if a deterministic demographic characterizationvector is available at deterministic demographic information availablestep 2610 and, if not, proceeds to update the demographiccharacteristics.

Referring to FIG. 26A, at read purchase ID info step 2620, the productID 2314 is read, and at update consumer demographic characterizationvector step 2630, an algorithm such as that represented in FIG. 24A isapplied to obtain a new demographic characterization vector 2366, whichis stored in the consumer profile 2360 at store updated demographiccharacterization vector step 2640.

The end test step 2650 can loop back to the read purchase ID info 2620if all the purchased products are not yet processed for updating, orcontinue to the branch for updating the product preference vector 2364.In this branch, the purchased product is identified at read purchase IDinfo step 2620. An algorithm, such as that illustrated in FIG. 24A forupdating the product preference vector 2364, is applied in updateproduct preference vector step 2670. The updated vector is stored inconsumer profile 2360 at store product preference vector step 2680. Thisprocess is carried out until all the purchased items are integrated inthe updating process.

FIG. 26B shows a flowchart for the correlation process. At step 2700 theadvertisement characteristics described earlier in accordance with FIG.23 along with the consumer ID are received by consumer profiling system2300. At step 2710 the demographic correlation 2356 is computed and atstep 2720 the product preference correlation 2358 is computed. Anillustrative example of an algorithm for correlation is presented inFIG. 24 b. The system returns demographic correlation 2356 and productpreference correlation 2358 to the advertisement records 2340 beforeexiting the procedure at end step 2750.

FIG. 27 illustrates two pricing schemes, one for content/opportunityprovider 160 based pricing 2770, which shows increasing cost as afunction of correlation. In this pricing scheme, the higher thecorrelation, the more the content/opportunity provider 2060 charges toair the advertisement.

FIG. 27 also illustrates consumer based pricing 2760, which allows aconsumer to charge less to receive advertisements which are more highlycorrelated with their demographics and interests.

As an example of the industrial applicability of the invention, aconsumer 2000 can purchase items in a grocery store that also acts as aprofiler 2040 using a consumer profiling system 2300. The purchaserecord is used by the profiler to update the probabilisticrepresentation of customer 2000, both in terms of their demographics aswell as their product preferences. For each item purchased by consumer2000, product characterization information in the form of a productdemographics vector and a product purchase vector is used to update thedemographic characterization vector and the product preference vectorfor consumer 2000.

A content/opportunity provider 2060 may subsequently determine thatthere is an opportunity to present an advertisement to consumer 2000.Content/opportunity provider 2060 can announce this opportunity toadvertiser 2044 by transmitting the details regarding the opportunityand the consumer ID 2312. Advertiser 2044 can then query profiler 2040by transmitting consumer ID 2312 along with advertisement specificinformation including the correlation request 2346 and ad demographicsvector 2348. The consumer profiling system 2300 performs a correlationand determines the extent to which the ad target market is correlatedwith the estimated demographics and product preferences of consumer2000. Based on this determination advertiser 2044 can decide whether topurchase the opportunity or not.

The principles of the present invention also provide novel ways ofcollecting subscriber information, e.g., subscribers have options tocontrol the flow of information. In one implementation, the subscribersdecide whether they want to be enrolled in the profiling, i.e., whetherthey want their viewing habits and other information to be collected.

In this implementation, the data is collected with the explicitpermission of the consumer/subscriber, who enrolls in the service andagrees to be profiled, similar to an “opt-in” feature. In the “opt-in”feature, the subscriber/consumer is specifically inquired whether he orshe wants to be profiled. In exchange for opt-in, the subscribers mayreceive economic benefit from the service through discounts on cableservice, discounts through retail outlets, rebates from specificmanufacturers, and other incentive plans.

In the case of video services, the subscribers may be presented with aseries of enrollment screens that confirm the subscribers' opt-in andask the subscriber for specific demographic information that may be usedto create one or more subscriber profiles.

In performing the enrollment process, it is possible to obtain specificdemographic information including household income, size, and agedistribution. Although this information is not necessary for profiling,obtaining it from the subscriber allows deterministic information to beused in conjunction with the probabilistic information.

Other opt-in methods may be used for the different media. In an Internetenvironment, a free browser add-on/plug-in may be used that performsprofiling through one or more secured techniques that remove cookies,alters/hides surf streams. In this case, the subscriber will have anoption to enroll in a secure system that permits profiling in acontrolled and secure manner along with providing economic incentivesfor participation in the profiling process. Upon enrolling in theservice, a profiling module may be downloaded or activated that mayperform the profiling through the browser.

The principles of the present invention also support the construction ofdistributed databases, each of which contain a portion of informationthat is utilized to create a subscriber/consumer profile. Thedistributed databases are constructed such that no privacy violatinginformation is contained in one database, and the operators utilized toextract information from each database preserve privacy and do notmeasure the parameters that should not be observed.

In the actual formation of subscriber profiles, the system may extractinformation from a plurality of databases and aggregate portions of theinformation to create a subscriber profile. In the aggregation of data,the emerging standards, such as XML, may be used for the transport ofthe data and the standardized profiles may be utilized to ensure thatthe secured server may effectively combine the elements of thedistributed profiling databases to create a compositeconsumer/subscriber characterization vector characterizing subscriberprofiles.

As illustrated in FIG. 29, the distributed database may be comprised ofspecific data sets including: purchase transaction data 2901 obtainedfrom a point-of-sale 2911 which may be a physical point-of-sale or avirtual (Internet) point-of-sale; Internet transaction data 2907obtained from a PC 2917 or other device connected to the Internet; videotransaction data 2905 obtained in conjunction with a television/set-topcombination 2925 or other video centric device; and demographic data2903 obtained from demographic data sources 2913. The examples ofdemographic data sources include commercial databases such as theMicroVision™ product from the Claritas Corporation™. Other public orprivate databases 2909 including those containing tax information mayalso be used. Different distributed databases are configured to a securecorrelation server (SCS) 2915.

In the present invention, Quantum Advertising™ is proposed wherein aprobabilistic representation of an individual's interests, inparticular, products and services, is utilized and specific privateinformation about the individual is kept private. In this way, it ispossible for advertisers to effectively target information to consumerswithout violating their privacy. The basis for what is termed QuantumAdvertising™ is derived from quantum mechanics, and in particular restson the concept that an individual's information may be treated in asimilar fashion to electrons and other subatomic particles. In quantummechanics, it is possible to have a probabilistic representation of aparticle, but impossible to have a deterministic representation in whichthe precise position of the particle is known.

In the present invention, the probabilistic descriptions of subscribersalong with a restricted set of operators are developed. The restrictedset of operators allows certain measurements to be made, but prohibitsprivacy invading determinations. As an example, an operator may becreated and utilized that may indicate a probability that an individualwill potentially purchase a new health care product, such as a shampooor a toothpaste, but proper construction of the database and operatorswould prohibit determination of the individual's exact income in orderto see if they are a potential purchaser of that product.

Another example would be the development of a target group for a newdrug, such as an HIV related product. The proper construction of thedatabases and operators may allow for the formation of a group ofindividuals likely to be receptive to the product, but would not allowidentification of individuals in the group, and the database would notcontain health related information such as HIV status.

Thus, the principles of the present invention utilize one or moreoperators that allow the measurement of certain parameters(non-deterministic parameters), but prohibit the measurement of otherparameters. In accordance with the principles of the present invention,the description of an individual/household may be contained in a vectorwhich is described as the ket vector, using the notation |A> where Arepresents the vector describing an aspect of the individual/household.

The ket vector |A> can be described as the sum of components such that

|A>=(a ₁ρ₁ +a ₂ρ₂ + . . . a _(n)ρ_(n))+(b₁σ₁+b₂σ₂+ . . .b_(n)σ_(n))+(c₁τ₁+c₂τ₂+ . . . c_(n)τ_(n))+(d₁ν₁+d₂ν₂+ . . .d_(n)ν_(n))+(e₁ω₁+e₂ω₂+ . . . e_(n)ω_(n))

where a_(n)ρ_(n) represents weighted demographic factors that may bedeterministic or probabilistic.

The other components of the ket vector |A> include:

b_(n)σ_(n), which represents weighted socio-economic factors;

c_(n)τ_(n), which represents weighted housing factors;

d_(n)ν_(n), which represents weighted purchase factors; and

e_(n)ω_(n), which represents weighted consumption factors.

The elements of the ket vector |A> may be stored on distributeddatabases, and the components within the groups above can be mixed andstored in various locations. In addition, |A> may not comprise all ofthe components listed above, but may instead utilize only a subset ofthat information.

Consistent with the concepts of wave functions in quantum mechanics, foreach ket vector there is a corresponding bra vector of the format <A|.In order to insure that the probabilities are normalized, the identity<A|A>=1 is insured. Although the ket and bra vectors are expected to bereal entities, there is the possibility of storing additionalinformation in a complex ket vector, in which case the corresponding bravector will be <A^(*)|, and the normalization criteria is <A*|A>=1.

Having created the basic descriptions of the households/individuals inthe form of a distributed or centralized database, a series of linearoperations may be performed on the database in order to obtain resultsthat provide targeting information. The linear operations may beperformed using one or more operators, which when applied to thedatabase, yield a measurable result. It is important to note that byproper construction of the operators, it is possible to preventinappropriate (privacy violating) measurements from being made.

The generalized method for obtaining information from the database isthus:

targeting information=<A|f|A>

where f is a single operation or series of operations that result in ameasurable quantity (observable). Through the application of theseoperators it is possible to query the database in a controlled mannerand obtain information about a target group, or to determine if anadvertisement is applicable to an individual/household (subscriber).

For determination of the applicability of an advertisement to anindividual/household, the advertiser can supply an ad characterizationvector along with the ID of an individual/household, with theapplicability of the advertisement being determined as:

ad applicability=<A|AC{ID}|A>

where AC {ID} is the ad characterization vector that contains an ID thatmay be at the individual, anonymous, or group level. Examples of thepossible IDs are as follows:

Individual Level:

-   -   social security #    -   address    -   credit card/courtesy card #    -   phone #

Anonymous (e.g. Through the Use of Anonymous Transaction Profiling):

-   -   transaction ID (video transaction records)    -   transaction ID (purchase transaction records)    -   transaction ID (surfing transaction records)

Group:

-   -   zip code    -   area code/central office code    -   domain name    -   employer

The use of individual/household IDs allows determination of theapplicability of an advertisement for a particular household orindividual. Anonymous transaction IDs may be used when no informationregarding the identity of the subscriber is being provided, but whentransaction profiles have been developed based on the use of anonymoustransaction profiling. Group IDs may be utilized to determineapplicability of an advertisement to a particular group, with the basisfor the grouping being geographic, demographic, socio-economic, orthrough another grouping mechanism.

The operators may result in a simple correlation operation in which theoperator contains an advertisement characterization vector which iscorrelated against elements in the database, or may be a series ofoperations which result in the determination of the applicability of anadvertisement, or determination of the product preferences of a group oran individual.

The ad characterization vector contains a description of the expectedcharacteristics of the target market. The ad characterization vector maybe obtained from the advertiser, a media buyer, or individual cognizantof the market to which the advertisement is directed.

Other operators can be constructed so that functions other thancorrelations can be performed. As an example, grouping or clustering canbe performed on the database by performing a series of operations thatidentifies consumers with similar characteristics. In addition togrouping or clustering, operators can be constructed to identify a setof subscribers who are candidates for a product based on specificselection criteria. As an example, it is possible to construct anoperator which returns a list of subscribers likely to be interested ina product, with the level of interest being determined fromprobabilistic elements such as age, income, previous purchase profiles,Internet profiles, or video selection profiles.

Proper construction of the database (and in particular construction ofthe ket vectors and ket vector subcomponents) and the operators ensuresthat privacy is maintained and prevents direct reading of the data andinappropriate queries. Furthermore, the actual transaction records (e.g.purchases, web surf streams, or channels viewed) are never stored, andno privacy violating information (e.g. medical conditions) are stored inthe database.

FIGS. 30A and 30B illustrate examples of demographic factors includinghousehold size and ethnicity. As previously mentioned, these functionsmay not necessarily be probabilistic, but may be obtained fromquestionnaires presented to the subscriber that lead to deterministicresponses. These responses can be represented as unity valueprobabilities.

The principles of the present invention propose advantages both forsubscribers and advertisers. A proposed service in accordance with theprinciples of the present invention will be free to the subscriber andincludes incentives such as discounts on Internet/video service.Furthermore, the advertisers may pay a premium for advertisements placedusing the system. This premium is amongst the content provider, theInternet/video service provider, and the provider of ad matching.

As an example, if an advertising opportunity during a network sportsevent costs $0.10 per viewer, the charge for the matched advertisement(ad) might be $0.14 per viewer. The additional $0.04 is divided amongstthe content provider (in this case the network), the Internet/videoservice provider, and the provider of ad matching. Because the ad is notdisplayed to the entire set of viewers, but rather to the subset ofviewers that will find the ad acceptable, the total cost to theadvertiser is likely to be less than, or at most the same as without thematching. The ad matching increases the effectiveness of the advertisingand thus makes better use of advertising dollars.

The service may be applied to cable networks, both for Internet basedservices as well as video services. For Internet based services overcable networks, the targeting may be at the level of the individualhome. For video services, the targeting is presently at the level of thenode, since cable networks do not have the individual home resolutionthat switched digital video networks have. Ad substitution technology atthe set-top level may increase the resolution of cable advertising,while SDV networks are inherently capable of resolution at theindividual home level.

In one embodiment, the present invention may be deployed as an AdManagement System (AMS) in a video environment. The AMS includes aSecure Correlation Server™ (SCS) configured to deliver targetedadvertisements over video systems including Switched Digital Video (SDV)platforms, cable platforms, satellite platforms, and streaming video(Internet) delivery platforms. The system allows for advertisers todeliver ad characterization vectors to the Secure Correlation Server™(SCS). The ad characterization vectors assist in determining theapplicability of the advertisements to a particular subscriber or groupof subscribers (e.g., node). The AMS performs the functions ofprioritizing, selling, scheduling, and billing of video advertisements.

In another embodiment, the present invention may be deployed as abrowser add-on/plug-in for the Internet environment. In this embodiment,the profiling is not completely blocked, but the subscriber is allowedto switch to a secured mode wherein the subscriber is profiled via asecure system. In return, subscribers receive economic benefit for theirparticipation.

In another embodiment, the present invention is a profiling product thatoperates at the point-of-purchase (retail outlet, mail order, or otherretail purchasing system) and produces profiles based on the purchasesof the subscriber. The specific purchases of the consumer are notstored, and the profiles are only utilized by authorized members.

In another embodiment, the principles of the present invention aredeployed as a secured credit card that may be utilized to monitorpurchase transactions of the subscribers and to ensure consumers thattheir purchase information will not be aggregated, but to allow them togain the benefits of secure profiling. By the use of a secured creditcard, consumers may allow profiling based on their purchase records.This embodiment ensures that the raw transaction data (detailed purchaserecords) is not stored.

By using a credit card that is part of the targeted advertisingbusiness, it is possible to track the purchases made by that consumer.Although it is preferable to discard the specific transaction data afterprofiling, use of a credit card associated with the targeted advertisingprocess allows for tracking of purchase activity by consumers who“opt-in”. The credit card may also be subsidized by the advertisingdollars, thus creating a low interest rate credit card, which would bean incentive to “opt-in”.

In this embodiment, advertisers may also be able to correlate theiradvertisements against consumer information and target advertisements tothe subscribers, however, the advertisers are not provided access to theprofiles themselves. The revenues generated by the credit cardissuer/profiler may be used to subsidize the credit card in the form ofdecreased interest rates and/or discounts or rebates for use of thecard. Another feature of the secured credit card is the ability todetermine if a displayed advertisement resulted in the purchase of anitem. As an example, if a targeted advertisement is displayed to aconsumer via the present system and the item is subsequently purchasedusing the secured credit card, the advertisement may be marked aseffective. On a statistical basis, the effectiveness of an advertisingcampaign may be readily measured when the subscribers receiveadvertisements through the secured system and make their purchase usingthe secured credit card of the present invention.

In one implementation, the present invention may be based on the use ofa secure correlation server (SCS) connected directly to an accessplatform, e.g., a Broadband Digital Terminal (BDT). In thisimplementation, the secure correlation server is capable of receivingvideo profiles (foiined from channel changes and dwell times) from theBDT as well as receiving consumer purchase records from participatingretail outlets and/or online stores. The SCS may also utilize the datafrom external databases.

In this implementation, the profiling is performed based on consent,e.g., the profiles of subscribers/consumers who opt-in for the serviceagree to have their demographic and preference profiles stored on theSCS.

Advertisers wishing to send advertisements to a subscriber during an adopportunity (Web page ad location or video advertising spot) transmit anad characterization vector to the SCS. The ad characterization vectormay be created by the advertiser by simply filling out a Web pagecontaining questions (with pull down answers) that describe the targetmarket by demographic information or by preference information.

Upon receiving the ad characterization vector the SCS correlates the adcharacterization vector with the subscriber/consumer characterizationvector. Based on the results of this correlation, the SCS may determinewhether the ad should be delivered to the subscriber, or if an alternatead should be presented.

A privacy firewall may be maintained between the BDT and the SCS toensure that subscriber/consumer characterization vectors may not be reador constructed by unauthorized parties. Because no raw data (consumerpurchase or viewing records) are stored on the SCS, there is nopossibility of unauthorized access of private information. This systemallows subscribers/consumers the ability to receive more desirableadvertisements while simultaneously receiving discounts forInternet/video services and at retail/online outlets. Advertisersreceive the benefit of more effective advertisements, and thus spendadvertising dollars more efficiently. This increase in efficiencyresults in increased revenue stream. Advertisers pay a premium fortargeted advertisements, as opposed to traditional linked sponsorshipadvertisements in which a flat rate is paid for access to an audiencewhose characteristics are only generally known.

Although this invention has been illustrated by reference to specificembodiments, it will be apparent to those skilled in the art thatvarious changes and modifications may be made, which clearly fall withinthe scope of the invention. The invention is intended to be protectedbroadly within the spirit and scope of the appended claims.

1. A method of managing an advertisement system, the method comprising:(a) accessing a plurality of consumer information records correspondingto a plurality of consumers, each consumer information record comprisingconsumer data in at least two information categories; (b) accessing anadvertisement and a corresponding ad characterization record, theadvertisement characterization record comprising target characteristicsfor the at least two information categories; (c) generating an indexscore for each information category in each consumer information recordby correlating the consumer data in the at least two informationcategories of the consumer information records with the targetcharacteristics for the at least two information categories in the adcharacterization record; (d) identifying, based on the index scores, atleast one target consumer to receive the advertisement.
 2. The method ofclaim 1, further comprising: (e) calculating an estimated consumerinterest value for each of the plurality of consumers by using acorrelation coefficient.
 3. The method of claim 2, wherein thecorrelation coefficient is the Pearson product-moment coefficient. 4.The method of claim 1, wherein the at least one target consumer isidentified by: (i) summing the index scores in each information categoryfor each consumer to determine a plurality of summed index scores; (ii)retrieving a predetermined summed index score corresponding to the adcharacterization record; and (iii) selecting at least one consumer witha summed index score equal to or greater than the predetermined summedindex score.
 5. The method of claim 4, wherein the summed index scoresreflect a probability that the corresponding consumer is interested inthe advertisement.
 6. The method of claim 1, wherein the index score foreach information category is determined through the use of one or morepreviously developed heuristic rules, wherein the previously developedheuristic rules relate consumer information to the targetcharacteristics in at least one of the information categories.
 7. Themethod of claim 6, wherein the previously developed heuristic rules areprobabilistic in nature.
 8. A method of managing an advertisementsystem, the method comprising: (a) accessing a plurality of consumerinformation records corresponding to a plurality of consumers, whereinthe plurality of consumer information records include demographicinformation and product preference information corresponding to therespective consumer; (b) generating a plurality of consumer index scoresbased on the consumer information records, wherein each of the consumerindex scores corresponds to the respective consumer; (c) accessing anadvertisement and a corresponding advertisement characterizationprofile, wherein the advertisement characterization profile comprisestarget characteristics of the corresponding advertisement; (d)generating an advertisement index score based on the advertisementcharacterization profile; and (e) calculating an estimated consumerinterest value for each of the plurality of consumers by correlating theplurality of consumer index scores with the advertisement index score.9. The method of claim 8, further comprising: (f) identifying, based onthe estimated consumer interest value, at least one target consumer toreceive the advertisement.
 10. The method of claim 8, wherein thecalculating the estimated consumer interest value uses a correlationcoefficient.
 11. The method of claim 10, wherein the correlationcoefficient is the Pearson product-moment coefficient.
 12. The method ofclaim 8, wherein the index scores reflect a probability that thecorresponding consumer is interested in a predefined interest category.13. The method of claim 12, wherein the index score is generated basedon one or more previously developed heuristic rules, wherein thepreviously developed heuristic rules relate consumer information to atarget characteristic of the predefined interest category.
 14. Themethod of claim 13, wherein the previously developed heuristic rules areprobabilistic in nature.
 15. A computer program product, comprising acomputer usable medium having a computer readable program code embodiedtherein, said computer readable program code adapted for execution on acomputer to implement a method of managing an advertisement system, saidmethod comprising: (a) accessing a plurality of consumer informationrecords corresponding to a plurality of consumers, each consumerinformation record comprising consumer data in at least two informationcategories; (b) accessing an advertisement and a corresponding adcharacterization record, the advertisement characterization recordcomprising target characteristics for the at least two informationcategories; (c) generating an index score for each information categoryin each consumer information record by correlating the consumer data inthe at least two information categories of the consumer informationrecords with the target characteristics for the at least two informationcategories in the ad characterization record; (d) identifying, based onthe index scores, at least one target consumer to receive theadvertisement.
 16. The computer program product of claim 15, furthercomprising: (e) calculating an estimated consumer interest value foreach of the plurality of consumers by using a correlation coefficient.17. The computer program product of claim 16, wherein the correlationcoefficient is the Pearson product-moment coefficient.
 18. The computerprogram product of claim 15, wherein the at least one target consumer isidentified by: (i) summing the index scores in each information categoryfor each consumer to determine a plurality of summed index scores; (ii)retrieving a predetermined summed index score corresponding to the adcharacterization record; and (iii) selecting at least one consumer witha summed index score equal to or greater than the predetermined summedindex score.
 19. An advertisement matching system in a computingenvironment, said system comprising: an electronic storage unitconfigured to store a plurality of consumer information records, therecords comprising consumer data in at least two information categories;a receiving unit configured to receive advertisement data including anadvertisement and a corresponding advertisement characterization record,the advertisement characterization record comprising targetcharacteristics for the at least two information categories; and aprocessor configured to: (i) interpret the consumer information recordsand the advertisement characterization record; (ii) generate an indexscore for each information category in each consumer information recordby correlating the consumer data in the at least two informationcategories of the consumer information records with the targetcharacteristics for the at least two information categories in theadvertisement characterization record; and (iii) identify, based on theindex scores, at least one target consumer to receive the advertisement.20. The advertisement matching system of claim 19, further comprising:an advertisement delivery unit for delivering the advertisement to theat least one target consumer.